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International audience; The atomic-scale characterization of surface active sites on γ-alumina still represents a great challenge for numerous catalytic applications. Here, we combine advanced density functional theory (DFT) calculations with one- and two-dimensional proton solid-state NMR experiments to identify the exact location and the spatial proximity of hydroxyl groups on γ-alumina crystallites. Our approach relies on revisited models for the (100), (111), basal (110)b, and lateral (110)l facets of γ-alumina, as well as for the edges at their intersections. Notably, we show that the ≃0 ppm AlTd-μ1-OH protons are predominantly located on edges, where these are free from the H-bond network. The proximities among the AlTd-μ1-OH as well as with μ2-OH groups are revealed by 1H–1H dipolar correlation experiments and analyzed in the light of the DFT calculations, which identify their location on the basal (110)b facet and on the (110)b/(100) and (110)b/(110)l edges. Using chlorine atoms to probe the presence of hydroxyls, we show that the chlorination occurs selectively by exchanging μ1-OH located on edges and on lateral (110)l facets. By contrast, the basal (110)b and lateral (111) facets are not probed by Cl. This exchange explains the disappearance of the ≃0 ppm peak and of the correlations involving AlTd-μ1-OH species. Moreover, after chlorination, a deshielding of the AlTd is observed on high-resolution 27Al NMR spectra. More subtle effects are visible on the proton correlation spectra, which are attributed to the disruption of the H-bond network in the course of chlorination.

Digital Twin (DT) is a pivotal application under the industrial digital transformation envisaged by the fourth industrial revolution (Industry 4.0). DT defines intelligent and real-time faithful reflections of physical entities such as industrial robots, thus allowing their remote control. Relying on the latest advances in Information and Communication Technologies (ICT), namely Network Function Virtualization (NFV) and Edge-computing, DT can be deployed as an on-demand service in the factories close proximity and offered leveraging radio access technologies. However, with the purpose of achieving the well-known scalability, flexibility, availability and performance guarantees benefits foreseen by the latest ICT, it is steadily required to experimentally profile and assess DT as a Service (DTaaS) solutions. Moreover, the dependencies between the resources claimed by the service and the relative demand and work loads require to be investigated. In this work, an Edge-based Digital Twin solution for remote control of robotic arms is deployed in an experimental testbed where, in compliance with the NFV paradigm, the service has been segmented in virtual network functions. Our research has primarily the objective to evaluate the entanglement among overall service performance and VNFs resource requirements, and the number of robots consuming the service varies. Experimental profiles show the most critical DT features to be the inverse kinematics and trajectory computations. Moreover, the same analysis has been carried out as a function of the industrial processes, namely based on the commands imposed on the robots, and particularly of their abstraction-level, resulting in a novel trade-off between computing and time resources requirements and trajectory guarantees. The derived results provide crucial insights for the design of network service scaling and resource orchestration frameworks dealing with DTaaS applications. Finally, we empirically prove LTE shortage to accommodate the minimum DT latency requirements. Supported by the EU through the 5Growth project (856709); the UNICO 5G I+D 6G-EDGEDT-01/02; the Spanish Ministry of Economic Affairs and Digital Transformation; NPRP-S 13th Cycle (NPRP13S-0205-200265) from the Qatar National Research Fund (a member of Qatar Foundation)

Medical Field, Robotic vision, Pattern recognition, Hurdle detection, and smart city are examples of areas that require image processing to achieve automation. Detecting an edge is an important stage in any computer vision application. The performance of the edge detecting algorithm is largely affected by the noise present in an image. An Image with a low signal-to-noise ratio (SNR), imposes a challenge to locate its edges. To improve the observable image boundaries, an adaptive filtering technique is proposed in this article. The proposed algorithm uses convolution of Gabor filter with Gaussian (GoG) operator to clean the noise before non-Maxima suppression. Furthermore, using variable hysteresis thresholding can further improve edge locating. The implementation of the algorithm was done by Python and Matlab. The obtained results were compared to a number of reviewed algorithms such as the Canny method, Laplacian of Gaussian, The Marr-Hildreth method, Sobel operator, and the Haar wavelet-based method. Three performance factors were used; PNSR, MSE, and processing time. The simulation result shows that the proposed method has higher PNSR, lower MSE, and shorter processing time when compared to the Canny detector, the Marr-Hildreth, Haar wavelet-based, Laplacian of Gaussian, and the Sobel operator methods. The higher PNSR, lower MSE, and shorter processing time mean improved edge details of the processed image.

In the realm of digital advancement, the Cloud-Edge-IoT continuum is seen as a key factor in shaping the future of technology. This continuum represents the evolution of data processing and management as data moves from IoT devices to Edge and Cloud. On 10 May 2023, Open Continuum under the EUCloudEdgeIoT initiativeand the European Commission organised a concertation meeting in Brussels, where the importance of shared knowledge and ideas in shaping the European research agenda for 2025-2027 was emphasised. This document gives a short overview of the Concertation meeting, including the main points highlighted by the more than 40 projects that presented, including the speaches fromPearse O’Donohue, Director for the Future Networks at the European Commission (EC), Manuel Mateo Goyet, Deputy Head of Unit Cloud and Software at EC, andMax Lemke, Head of Unit Internet of Things at EC. &nbsp

This study delves into the potential of 5G and blockchain technologies in smart agriculture, specifically targeting remote farming sectors. A conceptual architecture is proposed, aiming to leverage these cutting-edge technologies while ensuring energy efficiency and sustainable development within the agriculture industry. We provide an in-depth analysis of 5G applications and explore alternative communication channels that could empower remote communities, introducing them to state-of-the-art technological solutions. A unique aspect of our research is the detailed presentation of a parametric insurance business case, designed to align with the proposed architecture, thereby illustrating the practicality of our approach. Moreover, we propose an innovative solution to the challenge of providing internet connectivity in rural areas using Unmanned Aerial Vehicles (UAVs). Current limitations due to the weight of onboard equipment, which includes an access network and a backhaul link for internet provision, are addressed by introducing a lightweight 5G system onboard the UAV. This system serves multiple user equipment on the ground, with one acting as a connection gateway to the internet. This unique approach not only streamlines the process of providing rural internet connectivity but also opens up new markets for service providers and businesses related to lightweight 5G systems and UAV technology. Our work presents an avant-garde solution to technical challenges and offers significant business opportunities in the rapidly evolving telecommunications sector and beyond.

The idea of childhood as an innocent life stage, which developed during Romanticism, is still pervasive in the contemporary era and it influences children’s literature. This article provides further evidence for this by focusing on the specific production for young adults. Adolescence has its own characteristics that make it different from childhood: in particular, it is an in-between life stage, projected towards adulthood. Friedrich’s painting Wanderer above the Sea of Fog (1818) provides the visual composition at the basis of recent book covers for Young Adult novels, such as Ian McQue’s artwork for the Scholastic 2018 reissue of the Mortal Engines series (2001-2006) by Philip Reeve. The depiction of young protagonists in the same position as Friedrich’s wanderer shows how adults’ view on adolescence is influenced by the Romantic idea of childhood and, simultaneously, it conveys how adolescents feel attracted by adulthood and on the edge of their childhood., Linguæ & - Journal of Modern Languages and Cultures, V. 23 N. 1 (2023)

This report shares detailed results from a survey that was conducted with 700 European companies from January to March 2023 to help better understand the demand for CEI solutions, plans for future development, specific use cases of interest, and drivers and barriers to adoption. It also discusses the progress with regard to tasks that will be completed in May 2024's D1.3, which includes identifying business opportunities, analysing the competitiveness of the European CEI ecosystem, and analysing the gaps between supply and demand that should be addressed.

This deliverable describes the EU Demand Side Radar and its design and implementation activity. The online radar visualises use cases and demands scenarios based on CEI technologies, vertical sectors and readiness for implementation. Following the introduction, the document describes the steps taken to specify the design, collect the information and develop the EU Demand Side Radar. The core part of the document shows the latest development of the radar and the visualisation dashboard available on www.eucloudedgeiot.eu. In the last chapter of the document, the planning and the next steps of the EU demand side radar until the publication of the first official version are described.

Distributed infrastructures for computation and analytics are now evolving towards an interconnected ecosystem allowing complex applications to be executed on the Edge-to-Cloud continuum. Understanding and optimizing end-to-end performance in such a complex continuum is challenging. One crucial challenge is accurately reproducing relevant behaviors of a given application workflow and representative settings of the physical infrastructure underlying this complex continuum. This thesis is a conceptual and practical contribution to the Edge-to-Cloud continuum, proposing and applying methodologies in novel environments. Our methodologies aim at overcoming the complexity of understanding and optimizing Edge-to-Cloud workflows. As such, they enable reproducible experiment design, application optimization, efficient workflow provenance capture, and cost-effective experiment reproducibility. We validated our proposal by first developing the E2Clab framework that supports the complete analysis cycle of an application on the Edge-to-Cloud Continuum and then using E2Clab to optimize Pl@ntNet, a global plant identification application. Large-scale experimental validation on Grid'5000 shows that our methodology has proven helpful for understanding and improving the performance of Pl@ntNet.; Les infrastructures distribuées pour le calcul et l'analyse évoluent désormais vers un écosystème interconnecté permettant l'exécution d'applications complexes dans le continuum Edge-to-Cloud. Comprendre et optimiser les performances de bout en bout dans ce contexte est un défi majeur. Un besoin crucial consiste à reproduire avec précision les comportements pertinents des workflows et les paramètres représentatifs de l'infrastructure physique sous-jascente.Cette thèse est une contribution conceptuelle et pratique au continuum Edge-to-Cloud, proposant des méthodologies et les appliquant dans des environnements novateurs. Nos méthodologies ont pour but de s'affranchir de la complexité de la compréhension et de l'optimisation des workflows dans le continuum Edge-to-Cloud. Ainsi, elles permettent la conception d'expériences reproductibles, l'optimisation des applications, la capture efficace des données de provenance des exécutions de workflows, et la reproductibilité des expériences. Nous avons validé notre proposition avec, d'abord, le développement du framework E2Clab qui supporte le cycle complet d’analyse d’une application dans le continuum Edge-to-Cloud, et ensuite, l'utilisation de E2Clab pour l'optimisation Pl@ntNet, une application mondiale d'identification des plantes. La validation expérimentale à grande échelle sur Grid'5000 montre que notre méthodologie s'est avérée utile pour comprendre et améliorer les performances de Pl@ntNet.

Competitiveness in industry requires smooth, efficient, and high-quality operation. For some industrial applications or process control and monitoring applications, it is necessary to achieve high availability and reliability because, for example, the failure of availability in industrial production can have serious consequences for the operation and profitability of the company, as well as for the safety of employees and the surrounding environment. At present, many new technologies that use data obtained from various sensors for evaluation or decision-making require the minimization of data processing latency to meet the needs of real-time applications. Cloud/Fog and Edge computing technologies have been proposed to overcome latency issues and to increase computing power. However, industrial applications also require the high availability and reliability of devices and systems. The potential malfunction of Edge devices can cause a failure of applications, and the unavailability of Edge computing results can have a significant impact on manufacturing processes. Therefore, our article deals with the creation and validation of an enhanced Edge device model, which in contrast to the current solutions, is aimed not only at the integration of various sensors within manufacturing solutions, but also brings the required redundancy to enable the high availability of Edge devices. In the model, we use Edge computing, which performs the recording of sensed data from various types of sensors, synchronizes them, and makes them available for decision making by applications in the Cloud. We focus on creating a suitable Edge device model that works with the redundancy, by using either mirroring or duplexing via a secondary Edge device. This enables high Edge device availability and rapid system recovery in the event of a failure of the primary Edge device. The created model of high availability is based on the mirroring and duplexing of the Edge devices, which support two protocols: OPC UA and MQTT. The models were implemented in the Node-Red software, tested, and subsequently validated and compared to confirm the required recovery time and 100% redundancy of the Edge device. In the contrast to the currently available Edge solutions, our proposed extended model based on Edge mirroring is able to address most of the critical cases, where fast recovery is required, and no adjustments are needed for critical applications. The maturity level of Edge high availability can be further extended by applying Edge duplexing for process control.

This work explains the use of invariants in robotic perception and control skills. An 'invariant' is a mathematical constraint that remains unchanged under a particular transformation in a system. This property makes robotic functionalities more robust against 'disturbances' that cause these transformation. These invariants are stored in a world model (WM), which has a central role in the information architecture of the robot to share information between components. A 'robotic' football table serves as an example to illustrate the effectiveness of invariants. Despite looking different, all standard football tables satisfy the same set of invariants; the common layout of the field, line markings and puppets, which are not identical but satisfy the same set of constraints, such as parallelism, partial ordering, and relative colour and intensity differences between objects. During initialization, the invariants are actively identified and saved to the world model. During game play this world model is used by the perception and action skills and is updated when necessary. This work shows how the use of invariants creates robustness against variation in placement of the perception system and against variation in table dimensions and colours. When the camera is misaligned or moved mid-play, the world model is updated to ensure a smooth continuation of the game. The approach is tested on three standard football tables, with different dimensions and colours, showing that the approach is robust on standard tables that adhere to the invariants.

In many applications such as vanadium flow batteries, graphite acts as an electrocatalyst and its surface structure therefore determines the efficiency of energy conversion. Due to the heterogeneity of the material, activity descriptors cannot always be evaluated with certainty because the introduction of defects is accompanied by a change in surface chemistry. Moreover, surface defects occur in multiple dimensions, and their occurrence and influence on catalysis must be separated. In this work, we have studied the surface of graphite felt electrodes by different methods in terms of morphology and chemistry to understand the electrocatalytic activity. We then defined the interaction between the surface and the electronic structure with particular emphasis on the work function and valence band. Using model catalysts with different architectures, we established correlations between the electrocatalytic activity and the size of the conjugation and the orientation of the edges. Finally, it was possible to link the level of the work function to the electrocatalytic activity.

While the sociology of journalism has traditionally granted epistemic privilege to the mainstream news media, professional elites and their dominant meta-discourse, there is a recent trend to research journalism from other points of view. Researchers have investigated alternative and community media, journalists with low visibility or legitimacy in the workplace, as well as heterodox conceptions of professional excellence. These studies shed new light on little studied sub-groups and practices. How can we integrate them into a sustained scientific dialogue between researchers? This paper presents a methodologically original attempt to do so by dialogically re-interrogating material from studies of three situations where journalism absorbs precarious and politicised agents in the field (media activists), new practices and tasks that need doing (online discussion administration) and unusual kinds of professional attributes (experiencing emotionality in crisis reporting). In each case journalism’s pursuit of professional autonomy is at stake, since conditions of production clash with established professional myths and practices or generate incompatibilities with institutionalised expectations. Our three-way exchange using the bridging/sensitising concept of edge focuses on the conversion or convertibility of external forms of capital (legitimacies, resources and experiences) into forms redeemable and tradeable in a professional field. It exhibits how forces external to the journalistic profession are (made) present in each case but refracted differently in each local configuration. We call for a more systematic and relational study of these kinds of localised refractions.

Synchrotron radiation sources are widely used in interdisciplinary research, generating an enormous amount of data while posing serious challenges to the storage, processing, and analysis capabilities of the large-scale scientific facilities worldwide. A flexible and scalable computing architecture, suitable for complex application scenarios, combined with efficient and intelligent scheduling strategies, plays a key role in addressing these issues. In this work, we present a novel cloud–edge hybrid intelligent system (CEHIS), which was architected, developed, and deployed by the Big Data Science Center (BDSC) at the Shanghai Synchrotron Radiation Facility (SSRF) and meets the computational needs of the large-scale scientific facilities. Our methodical simulations demonstrate that the CEHIS is more efficient and performs better than the cloud-based model. Here, we have applied a deep reinforcement learning approach to the task scheduling system, finding that it effectively reduces the total time required for the task completion. Our findings prove that the cloud–edge hybrid intelligent architectures are a viable solution to address the requirements and conditions of the modern synchrotron radiation facilities, further enhancing their data processing and analysis capabilities.

The present document (D1.1) is the first report of WP1 of UNLOCK-CEI project covering the demand landscape analysis. UNLOCK-CEI’s ambition is to UNLOCK the potential for accelerating the deployment of the Cloud-to-Edge-IoT (CEI) computing continuum in Europe. The focus is on demand-side drivers and challenges that can help identify technology-driven innovation and business opportunities that will stimulate demand value chains. To this end, UNLOCK-CEI deals with several technological concepts surrounding cloud, edge and IoT. This report provides a comprehensive overview of the main technologies that are the core of UNLOCK-CEI, analysing these from a demand market perspective and providing insights on their evolution and maturity, their current and future uptake, the key use-cases, and the main drivers and barriers for the adoption. This report is generated leveraging IDC research on the CEI market and other major public sources., This document contains information in the form of the UNLOCK-CEI project findings, work and products and its use is strictly regulated by the UNLOCK-CEI Consortium Agreement and by Contract no. 101070571. The contents of this document are the sole responsibility of the UNLOCK-CEI consortium and can in no way be taken to reflect the views of the European Commission.

International audience; The advent of mobile computing has changed the shape of distributed computing. Mobile devices now have enough computational capabilities to take part of distributed computations. Thanks to their wireless networking interfaces, they can have direct interactions with their peers, self-organize in dynamic multi-hop networks, like the Internet of Things (IoT), the Edge, Mobile Ad hoc Networks (MANETs), etc. Such networks exhibit a number of inherent characteristics (mobility, unpredictability, scarce connections, etc) which make it difficult the use of their resources by (distributed) applications. Because existing middleware for distributed computing was most often designed to operate on clouds/grids/clusters, it can hardly be used in these networks. IDAWI belongs to a new class of middleware solutions which rely on decentralization to federate the resources of dynamic multi-hop networks, to the purpose of facilitating the design and implementation of distributed applications in networks of mobile devices. IDAWI goes further by proposing a collective stream-oriented computing model that matches the very nature of such networks. This permitted us to design, implement and describe in this article the architecture of a specific decentralized Web backend exposing as a whole the resources of a dynamic multi-hop network as a set of Web services. In order to profit from the stream-oriented and collective natures of IDAWI 's computation model, these Web services make a clever use of several standard Web technologies they rely on. All the concepts presented in this paper can be found in an Open Source reference implementation.

Hepatitis E virus (HEV) is the notable causative agent of acute and chronic hepatic, renal, pancreatic, neurological, and hematopoietic blood cell infections with high risk in immunocompromised patients. Hepatic failure is mostly documented among adults, pregnant women, and patients with preexisting liver disease. HEV is a positive sense RNA virus of 7.2 kb genome size with typically three open reading frames (ORFs) which play essential roles in viral replication, genome assembly, and transcription. The mutational substitution in the viral RNA genome makes more it difficult to understand the actual relationship in the host–virus association. ORFs of HEV encode different structural and non-structural proteins and one of them is the capsid protein which is coded by ORF2. The capsid protein mediates the encapsulation of the viral genome as well as being involved in virion assembly. In the current study, the ligand-based docking approach was employed to inhibit the active amino acids of the viral capsid protein. Depending upon S-score, ADMET profiling, and drug scanning, the top ten tetrapeptides were selected as potential drug candidates with no toxicity counter to HEV receptor protein. The S-score or docking score is a mathematical function which predicts the binding affinities of docked complexes. The binding affinity of the predicted drug–target complexes helps in the selectivity of the desired compound as a potential drug. The best two selected peptides (i.e., TDGH with S-score of −8.5 and EGDE with S-score of −8.0) interacted with the active site amino acids of the capsid protein (i.e., Arg399, Gln420, and Asp444). The molecular dynamics simulations of RMSD trajectories of TDGH–capsid protein and EDGE–capsid protein have revealed that both docked complexes were structurally stable. The study revealed that these tetrapeptides would serve as strong potential inhibitors and a starting point for the development of new drug molecules against the HEV capsid protein. In future, in vivo studies are needed to explore selected peptides as potential drug candidates.

Processing rapidly growing data encompasses complex workflows that utilize the Cloud for high-performance computing and the Fog and Edge devices for low-latency communication. For example, autonomous driving applications require inspection, recognition, and classification of road signs for safety inspection assessments, especially on crowded roads. Such applications are among the famous research and industrial exploration topics in computer vision and machine learning. In this work, we design a road sign inspection workflow consisting of 1) encoding and framing tasks of video streams captured by camera sensors embedded in the vehicles, and 2) convolutional neural network (CNN) training and inference models for accurate visual object recognition. We explore a matching theoretic algorithm named CODA [1] to place the workflow on the computing continuum, targeting the workflowprocessing time, data transfer intensity, and energy consumption as objectives. Evaluation results on a real computing continuum testbed federated among four Cloud, Fog, and Edge providers reveal that CODA achieves 50%-60% lower completion time, 33%-59% lower CO2 emissions, and 19%-45% lower data transfer intensity compared to two state-of-the-art methods.

The secondUNLOCK CEIwebinar was the occasion to present the activities towards the creation of an engaged community of CEI adopters to help understand the requirements for the future uptake of such technologies and provide insights to guide future research and innovation in this field.The event opened thefirst wave of Value Chain Adopter workshops scheduled between February and March 2023. The attached slides were presented at the event Learn more on the initiative:wwww.eucloudedgeiot.eu

Deliverable 2.1 describes the PLATOON reference architecture including the logical components in terms of functionalities and interfaces, their relationships and the description of the key processes related to data exchange and security. This document is mainly created to support the developers of reference implementation of PLATOON platform and pilots or other external stakeholders that want to be integrated with PLATOON and use its interoperability capabilities.

Deliverable 2.5 describes the second version of PLATOON reference architecture including the logical components in terms of functionalities and interfaces, their relationships and the description of the key processes related to data exchange and security. This document is mainly created to support the developers of reference implementation of PLATOON platform and pilots or other external stakeholders that want to be integrated with PLATOON and use its interoperability capabilities.

LoRaWAN is now a leading technology in IoT developments due to its low power consumption and simple deployment features. Despite being termed a long-distance technology, its coverage is often hampered by the harsh propagation environment. In such cases, a very cost-effective solution is to use mobile gateways for both data collection and delivery at specific places of the edge, avoiding the need for cellular communication modules. This article addresses the optimization problem of deciding how mobile gateways should move, and which edge devices should be visited (and when) for collected data to be fed into the edge distributed storage, while ensuring data usefulness. This optimization problem of gateway mobility with edge storage is formulated mathematically, and a deep reinforcement learning framework is proposed to solve it. Results show that the proposed framework can achieve near optimal results, in particular for large-dense deployments and not very strict time windows. The proposed deep reinforcement learning solution is able to capture underlying patterns, like device coverage and traffic, without prior knowledge of such information. info:eu-repo/semantics/publishedVersion

Edge computing is proposed as a technical enabler for meeting emerging network technologies (such as 5G and Industrial Internet of Things), stringent application requirements and key performance indicators (KPIs). It aims to alleviate the problems associated with centralized cloud computing systems by placing computational resources to the network’s edge, closer to the users. However, the complexity of distributed edge infrastructures grows when hosting containerized workloads as microservices, resulting in hard to detect and troubleshoot outages on critical use cases such as industrial automation processes. Observability aims to support operators in managing and operating complex distributed infrastructures and microservices architectures by instrumenting end-to-end runtime performance. To the best of our knowledge, no survey article has been recently proposed for distributed edge and containerized microservices observability. Thus, this article surveys and classifies state-of-the-art solutions from various communities. Besides surveying state-of-the-art, this article also discusses the observability concept, requirements, and design considerations. Finally, we discuss open research issues as well as future research directions that will inspire additional research in this area.

All-dielectric nanoantennas, consisting of high refractive index semiconductor material, are drawing a great deal of attention in nanophotonics. Owing to their ability to manipulate efficiently the flow of light within sub-wavelength volumes, they have become the building blocks of a wide range of new photonic metamaterials and devices. The interaction of the antenna with light is largely governed by its size, geometry, and the symmetry of the multitude of optical cavity modes it supports. Already for simple antenna shapes, unraveling the full modal spectrum using conventional far-field techniques is nearly impossible due to the spatial and spectral overlap of the modes and their symmetry mismatch with incident radiation fields. This limitation can be circumvented by using localized excitation of the antenna. Here, we report on the experimental near-field probing of optical higher order cavity modes (CMs) and whispering gallery modes (WGMs) in amorphous silicon nanoantennas with simple, but fundamental, geometrical shapes of decreasing rotational symmetry: a disk, square, and triangle. Tapping into the near-field using an aperture type scanning near-field optical microscope (SNOM) opens a window on a rich variety of optical patterns resulting from the local excitation of antenna modes of different order with even and odd parity. Numerical analysis of the antenna and SNOM probe interaction shows how the near-field patterns reveal the node positions of – and allows us to distinguish between – cavity and whispering gallery modes. As such, this study contributes to a richer and deeper characterization of the structure of light in confined nanosystems, and their impact on the structuring of the light fields they generate.

The article is devoted to modeling and visualization of the formation of flat-nosed (snub-nosed) dodecahedron (snub dodecahedron). The purpose of the research is to model the snub dodecahedron (flat-nosed dodecahedron) and visualize the process of its formation. The formation of the faces of the flat-nosed dodecahedron consists in the truncation of the edges and vertices of the Platonic dodecahedron with the subsequent rotation of the new faces around their centers. The values of the truncation of the dodecahedron edges, the angle of rotation of the faces and the length of the edge of the flat-nosed dodecahedron are the parameters of three equations composed as the distances between the vertices of triangles located between the faces of the snub dodecahedron. The solution of these equations was carried out by the method of successive approximations. The results of the calculations were used to create an electronic model of the flat-nosed dodecahedron and visualize its formation. The task was generally achieved in the AutoCAD system using programs in the AutoLISP language. Software has been created for calculating the parameters of modeling a snub dodecahedron and visualizing its formation.

Edge computing is a recent computing paradigm that brings cloud services closer to the client. Among other features, edge computing offers extremely low client/server latencies. To consistently provide such low latencies, services should run on edge nodes that are physically as close as possible to their clients. Thus, when the physical location of a client changes, a service should migrate between edge nodes to maintain proximity. Differently from cloud nodes, edge nodes integrate CPUs of different Instruction Set Architectures (ISAs), hence a program natively compiled for a given ISA cannot migrate to a server equipped with a CPU of a different ISA. This hinders migration to the closest node. We introduce H-Container, a system that migrates natively compiled containerized applications across compute nodes featuring CPUs of different ISAs. H-Container advances over existing heterogeneous-ISA migration systems by being (a) highly compatible – no user’s source-code nor compiler toolchain modifications are needed; (b) easily deployable – fully implemented in user space, thus without any OS or hypervisor dependency, and (c) largely Linux-compliant – it can migrate most Linux software, including server applications and dynamically linked binaries. H-Container targets Linux and its already-compiled executables, adopts LLVM, extends CRIU, and integrates with Docker. Experiments demonstrate that H-Container adds no overheads during program execution, while 10–100 ms are added during migration. Furthermore, we show the benefits of H-Container in real-world scenarios, demonstrating, for example, up to 94% increase in Redis throughput when client/server proximity is maintained through heterogeneous container migration.

Modern scientific workflows require hybrid infrastructures combining numerous decentralized resources on the IoT/Edge interconnected to Cloud/HPC systems (aka the Computing Continuum) to enable their optimized execution. Understanding and optimizing the performance of such complex Edge-to-Cloud workflows is challenging. Capturing the provenance of key performance indicators, with their related data and processes, may assist in understanding and optimizing workflow executions. However, the capture overhead can be prohibitive, particularly in resource-constrained devices, such as the ones on the IoT/Edge.To address this challenge, based on a performance analysis of existing systems, we propose ProvLight, a tool to enable efficient provenance capture on the IoT/Edge. We leverage simplified data models, data compression and grouping, and lightweight transmission protocols to reduce overheads. We further integrate ProvLight into the E2Clab framework to enable workflow provenance capture across the Edge-to-Cloud Continuum. This integration makes E2Clab a promising platform for the performance optimization of applications through reproducible experiments.We validate ProvLight at a large scale with synthetic workloads on 64 real-life IoT/Edge devices in the FIT IoT LAB testbed. Evaluations show that ProvLight outperforms state-of-the-art systems like ProvLake and DfAnalyzer in resource-constrained devices. ProvLight is 26 -- 37x faster to capture and transmit provenance data; uses 5 -- 7x less CPU; 2x less memory; transmits 2x less data; and consumes 2 -- 2.5x less energy. ProvLight and E2Clab are available as open-source tools.

A considerable number of systems are based on the widely used architecture of Fog and Edge Computing. As new uses of these architectures occur, systems incorporate new types of devices, making their configurations diverse and complex. While systems become better suited to specific needs, this heterogeneity poses new challenges as well. More types of devices mean not only more types of data, demanding proper processing, but also different hardware restrictions in processing power and network capacity to consider. All three challenges result in latency in data delivery, if disregarded. Geo-distributed data analytics is one of the most widespread uses of Fog and Edge Computing. However, the data processing frameworks it runs on are not designed for Fog and Edge Computing systems. Because of that, they do not utilize the resources of these systems efficiently. Improving resource utilization relies on the proper configuration of systems and use of suitable software. The production of results from the collected data requires complex workflows, which do not run as a whole, but as smaller tasks. Resources remain immutable, but tasks can be assigned to nodes in ways that allow them to run efficiently and in shorter times. This can be achieved by placing tasks with certain data types on nodes with specialized hardware, or on nodes with greater processing power when these tasks are computationally demanding. We present technical details on how someone can extend the existing schedulers of Spark and Kubernetes to assign tasks to arbitrary devices explicitly without requiring any changes to existing applications designed for each framework, so they can tailor task scheduling to their own infrastructure., Ένας σημαντικός αριθμός συστημάτων βασίζεται στην ευρέως χρησιμοποιούμενη αρχιτεκτονική των Fog και Edge Computing. Καθώς προκύπτουν νέες χρήσεις των αρχιτεκτονικών αυτών, τα συστήματα ενσωματώνουν νέους τύπους συσκευών, καθιστώντας τις διαμορφώσεις τους ποικιλόμορφες και πολύπλοκες. Παρά το γεγονός ότι τα συστήματα προσαρμόζονται καλύτερα σε συγκεκριμένες ανάγκες, αυτή η ετερογένεια δημιουργεί και νέες προκλήσεις. Οι περισσότεροι τύποι συσκευών μεταφράζονται όχι μόνο σε περισσότερους τύπους δεδομένων που απαιτούν κατάλληλη επεξεργασία, αλλά και σε διαφορετικούς περιορισμούς στην επεξεργαστική δύναμη και τη χωρητικότητα του δικτύου, οι οποίοι πρέπει να συνυπολογιστούν. Και οι τρεις προκλήσεις έχουν ως αποτέλεσμα την καθυστέρηση στην παράδοση δεδομένων, αν παραβλεφθούν. Η γεωκατανεμημένη ανάλυση δεδομένων είναι μια από τις πιο ευρέως διαδεδομένες χρήσεις των Fog και Edge Computing. Ωστόσο, τα πλαίσια επεξεργασίας δεδομένων στα οποία εκτελείται δεν έχουν σχεδιαστεί για συστήματα Fog και Edge Computing. Για το λόγο αυτόν δεν αξιοποιούν τους πόρους των συστημάτων αυτών αποδοτικά. Η βελτίωση της αξιοποίησης των πόρων βασίζεται στη σωστή παραμετροποίηση των συστημάτων και τη χρήση κατάλληλου λογισμικού. Η παραγωγή αποτελεσμάτων από τα συλλεχθέντα δεδομένα απαιτεί πολύπλοκες ροές εργασιών, οι οποίες δεν εκτελούνται ως σύνολο, αλλά ως μικρότερες διεργασίες. Οι πόροι παραμένουν αμετάβλητοι, αλλά οι διεργασίες μπορούν να ανατεθούν σε κόμβους με τρόπους που τους επιτρέπουν να τρέξουν αποδοτικά και σε πιο σύντομους χρόνους. Αυτό μπορεί να επιτευχθεί τοποθετώντας διεργασίες με συγκεκριμένους τύπους δεδομένων σε κόμβους με εξειδικευμένο υλικό, ή σε κόμβους με αυξημένη επεξεργαστική ισχύ όταν οι διεργασίες αυτές είναι υπολογιστικά απαιτητικές. Παρουσιάζουμε τεχνικές διευκρινίσεις για το πώς κάποιος μπορεί να επεκτείνει τους υπάρχοντες χρονοπρογραμματιστές των Spark και Kubernetes για να αναθέτουν ρητά εργασίες σε συγκεκριμένες συσκευές χωρίς να απαιτούνται αλλαγές στις υπάρχουσες εφαρμογές που έχουν σχεδιαστεί για αυτά τα πλαίσια, ώστε να προσαρμόσει τον προγραμματισμό των εργασιών στη δική του υποδομή.

Remote health monitoring systems play an important role in the healthcare sector. Edge computing is a key enabler for realizing these systems, where it is required to collect big data while providing real-time guarantees. In this study, we focus on remote cuff-less blood pressure (BP) monitoring through electrocardiogram (ECG) as a case study to evaluate the benefits of edge computing and compression. First, we investigate the state-of-the-art algorithms for BP estimation and ECG compression. Second, we develop a system to measure the ECG, estimate the BP, and store the results in the cloud with three different configurations: (i) estimation in the edge, (ii) estimation in the cloud, and (iii) estimation in the cloud with compressed transmission. Third, we evaluate the three approaches in terms of application latency, transmitted data volume, and power usage. In experiments with batches of 64 ECG samples, the edge computing approach has reduced average application latency by 15%, average power usage by 19%, and total transmitted volume by 85%, confirming that edge computing improves system performance significantly. Compressed transmission proved to be an alternative when network bandwidth is limited and edge computing is impractical.

With the lowering costs of sensors, high-volume and high-velocity data are increasingly being generated and analyzed, especially in IoT domains like energy and smart homes. Consequently, applications that require accurate short-term forecasts and predictions are also steadily increasing. In this paper, we provide an overview of a novel end-to-end platform that provides efficient ingestion, compression, transfer, query processing, and machine learning-based analytics for high-frequency and high-volume time series from IoT. The performance of the platform is evaluated using real-world dataset from RES installations. The results show the importance of high-frequency analytics and the surprisingly positive impact of error bounded lossy compression on machine learning in the form of AutoML. For example, when detecting yaw misalignments in wind turbines, an improvement of 9% in accuracy was observed for AutoML models on lossy compressed data compared to the current industry standard of 10-minute aggregated data. Thus, these small-scale experiments show the potential of the platform, and larger pilots are planned.

Different from the traditional natural scene images, optical remote-sensing images (RSIs) suffer from diverse imaging orientations, cluttered backgrounds, and various scene types. Therefore, the object-detection methods salient to optical RSIs require effective localization and segmentation to deal with complex scenarios, especially small targets, serious occlusion, and multiple targets. However, the existing models’ experimental results are incapable of distinguishing salient objects and backgrounds using clear boundaries. To tackle this problem, we introduce boundary information to perform salient object detection in optical RSIs. Specifically, we first combine the encoder’s low-level and high-level features (i.e., abundant local spatial and semantic information) via a feature-interaction operation, yielding boundary information. Then, the boundary cues are introduced into each decoder block, where the decoder features are directed to focus more on the boundary details and objects simultaneously. In this way, we can generate high-quality saliency maps which can highlight salient objects from optical RSIs completely and accurately. Extensive experiments are performed on a public dataset (i.e., ORSSD dataset), and the experimental results demonstrate the effectiveness of our model when compared with the cutting-edge saliency models.

The success of deep learning in extending the frontiers of artificial intelligence has accelerated the application of AI-enabled systems in addressing various challenges in different fields. In healthcare, deep learning is deployed on edge computing platforms to address security and latency challenges, even though these platforms are often resource-constrained. Deep learning systems are based on conventional artificial neural networks, which are computationally complex, require high power, and have low energy efficiency, making them unsuitable for edge computing platforms. Since these systems are also used in critical applications such as bio-medicine, it is expedient that their reliability is considered when designing them. For biomedical applications, the spatio-temporal nature of information processing of spiking neural networks could be merged with a fault-tolerant 3-dimensional network on chip (3D-NoC) hardware to obtain an excellent multi-objective performance accuracy while maintaining low latency and low power consumption. In this work, we propose a reconfigurable 3D-NoC-based neuromorphic system for biomedical applications based on a fault-tolerant spike routing scheme. The performance evaluation results over X-ray images for pneumonia (i.e., COVID-19) detection show that the proposed system achieves 88.43% detection accuracy over the collected test data and could be accelerated to achieve 4.6% better inference latency than the ANN-based system while consuming 32% less power. Furthermore, the proposed system maintains high accuracy for up to 30% inter-neuron communication faults with increased latency.

White‐tailed deer (Odocoileus virginianus) populations have recovered to about 30 million animals in the United States, but land cover has changed during the interval of recovery. To address the relationship between deer densities and current land cover at regional scales, I applied random forests and extreme gradient boosting classifiers to model low and high deer density classes, at two different thresholds (5.8 and 11.6 deer/km2), and land classes in three regions during approximately 2003. For low and high deer density classes divided at 5.8 deer/km2, deciduous broadleaf forest overall was the most influential and positive variable in the central east and central regions and crop and pasture were the most influential and negative variables in the southeast region. Deer density increased with area of deciduous and mixed forests, woody wetlands, and shrub in all regions. Deer density decreased with area of crop, developed open space, and developed low and medium residential density in all regions. For density classes divided at 11.6 deer/km2, deer density had the strongest relationship with woody wetlands in the central east region, mixed and deciduous forest in the southeast region, and woody wetlands and herbaceous vegetation in the central region. Deer density increased with deciduous and mixed forests, woody wetlands, and shrub in all regions. Conversely, deer density decreased with herbaceous vegetation, crop, and developed low residential densities in all regions. Therefore, at regional scales, deer overall occurred at greater densities in forests and woody wetlands and lower densities in agricultural and residential development, which did not appear to support more deer. Deer preference for forests does result in damage to forest products, but alternatively, some may consider that deer provide important socioeconomic and ecological services by reducing number of small trees, particularly in the absence of other disturbances that historically controlled tree biomass., At regional scales, deer occurred at greater densities in forests and lower densities in agricultural and residential development, which did not improve habitat by supporting more deer. Deer preference for forests does result in damage to forest products. However, deer provide important socioeconomic and ecological services by reducing number of small trees, particularly in the absence of other disturbances that historically controlled tree biomass.

This paper presents a method for estimating the six Degrees of Freedom (6DoF) pose of texture-less objects from a monocular image by using edge information. The deep learning-based pose estimation method needs a large dataset containing pairs of an image and ground truth pose of objects. To alleviate the cost of collecting a dataset, we focus on the method using a dataset made by computer graphics (CG). This simulation-based method prepares a thousand images by rendering the computer-aided design (CAD) data of the object and trains a deep-learning model. As an inference stage, a monocular RGB image is entered into the model, and the object’s pose is estimated. The representative simulation-based method, Pose Interpreter Networks, uses silhouette images as the input, thereby enabling common feature (contour) extraction from RGB and CG images. However, estimating rotation parameters is less accurate. To overcome this problem, we propose a method to use edge information extracted from the object’s ridgelines for training the deep learning model. Since edge distribution changes largely according to the pose, the estimation of rotation parameters becomes more robust. Through an experiment with simulation data, we quantitatively proved the accuracy improvement compared to the previous method (error rate decreases at a certain condition are translation 22.9% and rotation: 43.4%). Moreover, through an experiment with physical data, we clarified the issues of this method and proposed an effective solution by fine-tuning (error rate decrease at a certain condition are translation 20.1% and rotation 57.7%).

In this paper, the characteristics of the Excellence in Design for Greater Efficiencies (EDGE) green building certification system in terms of the scope of certification, and environmental and social strategies focused on 6024 units in the 17 EDGE-certified residential buildings in South Africa was investigated. This research analyzed the efficiency of energy, water, embodied energy in materials, and annual CO2 emissions and their contribution to solving housing issues in the given context. This research revealed that the design of EDGE-certified buildings focuses on the “efficiency first” approach and reduced energy and water demand more than on-site energy production. In energy-saving strategies, energy-saving solutions are three times (69.2%) higher than energy-generating solutions (23.1%). The results on resource savings showed that, on average, embodied energy in materials (54%) is the most reduced resource, followed by water (31%) and energy (29.7%). The statistical analysis indicated that there is no statistically significant correlation between energy, water, embodied energy in materials, and CO2 savings with floor areas of the buildings. In addition, this research highlighted how EDGE certification has positively contributed to solving South Africa’s resource shortage and affordable housing scarcity problems.

This report summarises the main highlights of the first webinar of The European Cloud, Edge & IoT Continuum, organised by UNLOCK CEI. Through the Cloud, Edge & IoT Continuum, Europe has a unique opportunity to regain a significant role in the computing market and retain digital autonomy. Cloud, Edge and IoT technologies are critical for Europe’s future. Due to the amount of data generated and the quick responses required, many companies’ IoT use cases for the next few years will leverage IoT and require both Cloud and Edge technologies. Many of these use cases bridge Cloud and Edge, and the three technologies already blend in several instances,impacting different domains - from energy infrastructures to national security to healthcare and economics in larger terms. Finally, these technologies also play an essential role in achieving sustainability goals. However, despite widespread adoption, IoT and Edge technologies are still in their very early application stages among companies and specific needs and requirements of possible adopters and demand side representative still have to be fully understood . As to the supply side, Cloud-Edge-IoT creates a wide range of opportunities for different companies by also incorporating many other enabling technologies. However, because this is still a relatively fragmented market with no dominant player, EU policies can influence its future shape. Learn more here Join our Value Chain Adopter groups and help us define Cloud-Edge-IoT demand requirements here

Maximizing the benefits of AI for Industry 4.0 is about more than just developing effective new AI methods. Of equal importance is the successful integration of AI into production environments. One open challenge is the dynamic deployment of AI on industrial edge devices within close proximity to manufacturing machines. Our IIP-Ecosphere1 platform was designed to overcome limitations of existing Industry 4.0 platforms. It supports flexible AI deployment through employing a highly configurable low-code based approach, where code for applications is generated improving their robustness. However, the performance of an AI is often upper bounded by the available computational resources. In this paper, we measure the performance of our platform on an industrial demonstrator and discuss the impact of deploying AI from a central server to the edge. As result, AI inference automatically deployed on an industrial edge is possible, but in our case three times slower than on a desktop computer, requiring still more optimizations. Replication package for equally named paper on Symposium of Software Performance 2022

September 19 2022 saw the first webinar of the EUCloudEdgeIoT.eu initiative, which aims at fostering the business and technical convergence of Cloud, Edge and IoT (CEI) in Europe. The webinar presented the goals of this newly launched initiative, explained its interaction with the wider policy context and clarified benefits and opportunities of getting involved in the planned activities. The event was organised by UNLOCK CEI, one of the projects part of the initiative. The attached slides were presented at the event Learn more on the initiative: wwww.eucloudedgeiot.eu

Recent advances in information technology have revolutionized the automotive industry, paving the way for next-generation smart vehicular mobility. Specifically, vehicles, roadside units, and other road users can collaborate to deliver novel services and applications that leverage, for example, big vehicular data and machine learning. Relatedly, fifth-generation cellular networks (5G) are being developed and deployed for low-latency, high-reliability, and high bandwidth communications. While 5G adjacent technologies such as edge computing allow for data offloading and computation at the edge of the network thus ensuring even lower latency and context-awareness. Overall, these developments provide a rich ecosystem for the evolution of vehicular applications, communications, and computing. Therefore in this work, we aim at providing a comprehensive overview of the state of research on vehicular computing in the emerging age of 5G and big data. In particular, this paper highlights several vehicular applications, investigates their requirements, details the enabling communication technologies and computing paradigms, and studies data analytics pipelines and the integration of these enabling technologies in response to application requirements.

Existing approaches for allocating resources on edge environments are inefficient and lack the support of heterogeneous edge devices, which in turn fail to optimize the dependency on cloud infrastructures or datacenters. To this extent, we propose in this paper OpERA, a multi-layered edge-based resource allocation optimization framework that supports heterogeneous and seamless execution of offloadable tasks across edge, fog, and cloud computing layers and architectures. By capturing offloadable task requirements, OpERA is capable of identifying suitable resources within nearby edge or fog layers, thus optimizing the execution process. Throughout the paper, we present results which show the effectiveness of our proposed optimization strategy in terms of reducing costs, minimizing energy consumption, and promoting other residual gains in terms of processing computations, network bandwidth, and task execution time. We also demonstrate that by optimizing resource allocation in computation offloading, it is then possible to increase the likelihood of successful task offloading, particularly for computationally intensive tasks that are becoming integral as part of many IoT applications such robotic surgery, autonomous driving, smart city monitoring device grids, and deep learning tasks. The evaluation of our OpERA optimization algorithm reveals that the TOPSIS MCDM technique effectively identifies optimal compute resources for processing offloadable tasks, with a 96% success rate. Moreover, the results from our experiments with a diverse range of use cases show that our OpERA optimization strategy can effectively reduce energy consumption by up to 88%, and operational costs by 76%, by identifying relevant compute resources.

Edge computing constitutes a promising paradigm of managing and processing the massive amounts of data generated by Internet of Things (IoT) devices. Data and computation are moved closer to the client, thus enabling latency- and bandwidth-sensitive applications. However, the distributed and heterogeneous nature of the edge as well as its limited resource capabilities pose several challenges in implementing or choosing an efficient edge-enabled storage system. Therefore, it is imperative for the research community to contribute to the clarification of the purposes and highlight the advantages and disadvantages of various edge-enabled storage systems. This work aspires to contribute toward this direction by presenting a performance analysis of three different storage systems, namely MinIO, BigchainDB, and the IPFS. We selected these three systems as they have been proven to be valid candidates for edge computing infrastructures. In addition, as the three evaluated systems belong to different types of storage, we evaluated a wide range of storage systems, increasing the variability of the results. The performance evaluation is performed using a set of resource utilization and Quality of Service (QoS) metrics. Each storage system is deployed and installed on a Raspberry Pi (small single-board computers), which serves as an edge device, able to optimize the overall efficiency with minimum power and minimum cost. The experimental results revealed that MinIO has the best overall performance regarding query response times, RAM consumption, disk IO time, and transaction rate. The results presented in this paper are intended for researchers in the field of edge computing and database systems.

Development of an Edge Computing system for strawberry (Fragaria) crop management under greenhouse, which allows the farmer to optimize resources and reduce costs by automating the monitoring of environmental variables and irrigation control. The data were collected by IoT devices based on the ESP8266 microcontroller and processed at the Edge of the cloud by an Edge server based on Android and ARM architecture, and then sent to the persistent database, which was configured as a data source for the Data Studio visualization tool. The system was configured to interact with the user through a graphical interface via a web application. Different Industry 4.0 enablers were used that interacted with each other such as robotics, sensors, Internet of Things and Big data. Two methodologies were used for the development of the proposal, SCRUM and POC. Desarrollo de un sistema Edge Computing para el manejo de cultivo de Fresa (Fragaria) bajo invernadero, que permite al agricultor optimizar recursos y reducir costos por medio de la automatización del monitoreo de las variables ambientales y el control del riego. Los datos fueron recolectados por dispositivos IoT basados en el microcontrolador ESP8266 y procesados al borde de la nube por un servidor Edge basado en Android y arquitectura ARM, para posteriormente ser enviados a la base de datos persistente, esta fue configurada como fuente de datos de la herramienta de visualización Data Studio. Se configuró el sistema para interactuar con el usuario por medio de una interfaz gráfica por medio de una aplicación web. Se utilizaron diferentes habilitadores de la industria 4.0 que interactuaron entre sí como robótica, sensores, Internet de las Cosas y Big data. Para el desarrollo de la propuesta se utilizaron dos metodologías, SCRUM y POC.

Cloud, edge and Internet of Things (IoT) technologies have emerged to overcome the challenges involved in sharing computational resources and information services. Within generic cloud systems, two models have been identified as having widespread applicability: computation clouds and data clouds. A data cloud is cloud computing that aims to manage, unify and operate multiple data workloads. Many current applications generate datasets consisting of petabytes (PB) of information. Managing large datasets is a complex issuel; in particular, datasets associated with many applications can be distributed widely in geographical terms, particularly in IoT systems. Edge and IoT systems are facing new challenges with increased complexity, making scalability an important issue that will affect the performance of the system. Data replication services are widely accepted techniques to improve availability and fault tolerance, and to improve the data access time. Current replication services, however, often exhibit an increase in response time, reflecting the problems associated with the ever-increasing size of databases. This paper proposes a prediction model to predict replica locations using the files’ access profile, which feeds the neural networks with the access and location behavior (file profile) to minimize the overhead of transferring large volumes of data, which slows down the system and requires careful management. This new model has shown high accuracy and low overheads. The result shows a significant improvement in total task execution time using the proposed model for locating files by 16.34% and 30.45%; in addition, the results show bandwidth improvement by 24.7% and 49.4% compared to the user profile prediction model and replica service model without prediction, respectively. Consequently, the proposed algorithm can improve data access speed, reduce data access latency and decrease bandwidth consumption.

With increasing demand for video streaming applications, exploiting cached content at the network edge becomes paramount to prevent congestion in the link between the wireless access network and the content providers. However, it is often challenging to exploit the caches in current client-driven video streaming solutions due to two key reasons. First, even those clients interested in the same content might request different quality levels as a video content is encoded into multiple qualities to match a wide range of network conditions and device capabilities. Second, the clients, who select the quality of the next chunk to request, are unaware of the cached content at the network edge. Hence, it becomes imperative to develop network-side solutions to exploit caching, in particular for the scenarios in which multiple video clients compete for some bottleneck capacity. In this article, we propose EdgeDASH which is a network-side control logic running at a WiFi AP to facilitate the use of cached video content. In particular, an AP can assign a client station to a video quality different than its request, in case the alternative quality provides a better utility. This includes, for example, a function of bits delivered from the cache, video bit rate, and the buffer stalls. We formulate the quality assignment problem as an optimization problem and develop several heuristics with polynomial complexity. Our simulations show that EdgeDASH facilitates significant cache hits and decreases the buffer stalls only by changing the client's request by one quality level, however with some increase in session instability. From our analysis, we conclude that EdgeDASH benefits are more visible especially when the clients with identical interests compete for a bottleneck link's capacity, over the baseline where the clients determine the quality adaptation.

In the upcoming 6G era, edge artificial intelligence (AI), as a key technology, will be able to deliver AI processes anytime and anywhere by the deploying of AI models on edge devices. As a hot issue in public safety, person re-identification (Re-ID) also needs its models to be urgently deployed on edge devices to realize real-time and accurate recognition. However, due to complex scenarios and other practical reasons, the performance of the re-identification model is poor in practice. This is especially the case in public places, where most people have similar characteristics, and there are environmental differences, as well other such characteristics that cause problems for identification, and which make it difficult to search for suspicious persons. Therefore, a novel end-to-end suspicious person re-identification framework deployed on edge devices that focuses on real public scenarios is proposed in this paper. In our framework, the video data are cut images and are input into the You only look once (YOLOv5) detector to obtain the pedestrian position information. An omni-scale network (OSNet) is applied through which to conduct the pedestrian attribute recognition and re-identification. Broad learning systems (BLSs) and cycle-consistent adversarial networks (CycleGAN) are used to remove the noise data and unify the style of some of the data obtained under different shooting environments, thus improving the re-identification model performance. In addition, a real-world dataset of the railway station and actual problem requirements are provided as our experimental targets. The HUAWEI Atlas 500 was used as the edge equipment for the testing phase. The experimental results indicate that our framework is effective and lightweight, can be deployed on edge devices, and it can be applied for suspicious person re-identification in public places.

Artificial intelligence is used in many different aspects of life these days. It is also increasingly used to create intelligent embedded devices. Their main task is to demonstrate intelligent features using limited hardware resources. This paper introduces the possibility of using AI on the Edge as a system to warn of low voltage on the battery in a stand-alone metering station powered by solar panels. The paper presents theoretical knowledge about artificial intelligence on the Edge, time series and algorithms in time series forecasting. In addition, a practical approach is presented in the Approach section. It also describes the stand-alone measurement station that was used, what type of data was collected and used, and what type of the time series forecasting algorithm was used. The main advantage of the approach is the practical use of Holt’s Linear Trend method in battery voltage forecasting. Work on the project is still in progress therefore the results presented in the paper are generated during simulation.

A precise prediction of the health status of industrial equipment is of significant importance to determine its reliability and lifespan. This prediction provides users information that is useful in determining when to service, repair, or replace the unhealthy equipment’s components. In the last decades, many works have been conducted on data-driven prognostic models to estimate the asset’s remaining useful life. These models require updates on the novel happenings from regular diagnostics, otherwise, failure may happen before the estimated time due to different facts that may oblige rapid maintenance actions, including unexpected replacement. Adding to offline prognostic models, the continuous monitoring and prediction of remaining useful life can prevent failures, increase the useful lifespan through on-time maintenance actions, and reduce the unnecessary preventive maintenance and associated costs. This paper presents the ability of the two real-time tiny predictive analytics models: tiny long short-term memory (TinyLSTM) and sequential dense neural network (DNN). The model (TinyModel) from Edge Impulse is used to predict the remaining useful life of the equipment by considering the status of its different components. The equipment degradation insights were assessed through the real-time data gathered from operating equipment. To label our dataset, fuzzy logic based on the maintainer’s expertise is used to generate maintenance priorities, which are later used to compute the actual remaining useful life. The predictive analytic models were developed and performed well, with an evaluation loss of 0.01 and 0.11, respectively, for the LSTM and model from Edge Impulse. Both models were converted into TinyModels for on-device deployment. Unseen data were used to simulate the deployment of both TinyModels. Conferring to the evaluation and deployment results, both TinyLSTM and TinyModel from Edge Impulse are powerful in real-time predictive maintenance, but the model from Edge Impulse is much easier in terms of development, conversion to Tiny version, and deployment.

Teorija grafova bitno je područje u matematici za prikaz podataka i odnosa između objekata. Planarni grafovi važna su vrsta grafova i postoje mnogi programi pomoću kojih je moguće prikazati takve grafove na dvodimenzionalnoj površini ekrana. U ovom radu su detaljnije opisana tri programa otvorenog koda: NetworkX, Boost Graph Library i JGraph, opisani su algoritmi koje oni koriste, i njihova međusobna usporedba. Testiran je njihov rad na primjerima grafova i prikazano je kakve rezultate daju. Graph theory is an important area in mathematics for presenting data and relationship between objects. Planar graphs are an important type of graphs and there are many programs that can be used to display such graphs on a two-dimensional screen surface. This paper describes in more detail three open source programs: NetworkX, Boost Graph Library and JGraph, describes the algorithms they use, and their comparison with each other. Their work was tested on examples of graphs and it is shown what results they give.

The article analyzes the results of the use of digital technologies in international trade. At the heart of the new technological revolution are digital technologies, especially the Internet of Things and blockchain. The Internet of Things (IoT) is a revolutionary networking technology of people, devices, physical and virtual objects, processes, and systems that can communicate with each other through data transmission. The Internet of Things allows these objects to communicate, exchange data, and analyze information about the world around them through networks and cloud software platforms. This technology helps solve many productions and household problems, gives control over the supply chain, prevents equipment failures, and opens up opportunities to sell new digital products and services. Under the influence of digitalization, international trade has become more fluid, global chains have become more flexible, delivery times for goods have been reduced, smart contracts have become widely used, and credit has become more accessible as a means of financing trade. With the advent of digital technologies and the transformation of the Internet into an important operational tool, the global economy is undergoing major changes. New technologies have changed consumer habits and also made life easier for suppliers. There are new online markets, new products, and new business models based on these technologies. E-commerce has rapidly outgrown thanks to the widespread use of the Internet and connected devices, which have provided direct access to online marketplaces for suppliers and buyers. This allowed them to manufacture, sell and distribute their products at low prices, making it easier to enter the market and diversify production. As a result, more and more goods and services are delivered remotely, often from overseas., {"references":["1.\tNurullayev J., Babayev H., İmpact of digital transformation on the labor market: a review, Journal of Global Economics, Management and Business Research , 13(4): 92-96, 2021, ISSN: 2454-2504","2.\tДовгаль В.А., Довгаль Д.В. Интернет Вещей: концепция, приложения и задачи, ISSN 2410-3225 Ежеквартальный рецензируемый, реферируемый научный журнал «Вестник АГУ». Выпуск 1 (216) 2018","3.\tSun, S.; Du, R.; Chen, S. A Secure and Computable Blockchain- Based Data Sharing Scheme in IoT System. Information 2021, 12, 47. https://doi.org/10.3390/info12020047","4.\tKasznik, E. 5 ways the 'Internet of Things' transformed the vending machine [Electronic resource] / E. Kasznik // Bizjournals. – URL: http://www.bizjournals. com/bizjournals/how-to/technology/2015/ 04/the-internet-of-things-is-transformingvending.html","5.\tNamiot D. et al. Blockchain applications for transport industry","6.\t//International Journal of Open Information Technologies. – 2017. – Т. 5. – №. 12. – С. 130-134."]}